Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v2
+      type: PandaReachDense-v2
+    metrics:
+    - type: mean_reward
+      value: -0.72 +/- 0.36
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v2**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

a2c-PandaReachDense-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f70430259842fe3a1511b9acb8ff4d300e19f54ae27cd6c3c27bc32d2663561c
+size 108157

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,95 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7dca9fd13b50>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7dca9fd15200>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690621684766307599,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.37580416 -0.0100023   0.5545293 ]\n [ 0.37580416 -0.0100023   0.5545293 ]\n [ 0.37580416 -0.0100023   0.5545293 ]\n [ 0.37580416 -0.0100023   0.5545293 ]]",
+        "desired_goal": "[[ 1.0106438   0.5184516  -1.5758194 ]\n [-1.5280068  -0.87355614  0.6943761 ]\n [-0.7776506   1.1198059  -0.03986908]\n [ 1.7085036   0.19300558 -1.0220121 ]]",
+        "observation": "[[ 3.7580416e-01 -1.0002302e-02  5.5452931e-01  1.6309988e-02\n   3.2235571e-04  1.3100060e-02]\n [ 3.7580416e-01 -1.0002302e-02  5.5452931e-01  1.6309988e-02\n   3.2235571e-04  1.3100060e-02]\n [ 3.7580416e-01 -1.0002302e-02  5.5452931e-01  1.6309988e-02\n   3.2235571e-04  1.3100060e-02]\n [ 3.7580416e-01 -1.0002302e-02  5.5452931e-01  1.6309988e-02\n   3.2235571e-04  1.3100060e-02]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[-0.13358629 -0.02352318  0.03269866]\n [ 0.11605325 -0.14102837  0.2625782 ]\n [ 0.02640063 -0.0861482   0.06985985]\n [ 0.05207201  0.13856235  0.25286996]]",
+        "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
+    },
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 50000,
+    "n_steps": 5,
+    "gamma": 0.99,
+    "gae_lambda": 1.0,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false,
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7319f09ad010caaa3106b5e3a6910da4298fe1ca263cf70a7bd7b464e61d669f
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b965db73fd4df2ca2e8b87bab2d6fd1da3954df1c9daf3b50aa1475f5a52388f
+size 46014

a2c-PandaReachDense-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaReachDense-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7dca9fd13b50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7dca9fd15200>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690621684766307599, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.37580416 -0.0100023   0.5545293 ]\n [ 0.37580416 -0.0100023   0.5545293 ]\n [ 0.37580416 -0.0100023   0.5545293 ]\n [ 0.37580416 -0.0100023   0.5545293 ]]", "desired_goal": "[[ 1.0106438   0.5184516  -1.5758194 ]\n [-1.5280068  -0.87355614  0.6943761 ]\n [-0.7776506   1.1198059  -0.03986908]\n [ 1.7085036   0.19300558 -1.0220121 ]]", "observation": "[[ 3.7580416e-01 -1.0002302e-02  5.5452931e-01  1.6309988e-02\n   3.2235571e-04  1.3100060e-02]\n [ 3.7580416e-01 -1.0002302e-02  5.5452931e-01  1.6309988e-02\n   3.2235571e-04  1.3100060e-02]\n [ 3.7580416e-01 -1.0002302e-02  5.5452931e-01  1.6309988e-02\n   3.2235571e-04  1.3100060e-02]\n [ 3.7580416e-01 -1.0002302e-02  5.5452931e-01  1.6309988e-02\n   3.2235571e-04  1.3100060e-02]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[-0.13358629 -0.02352318  0.03269866]\n [ 0.11605325 -0.14102837  0.2625782 ]\n [ 0.02640063 -0.0861482   0.06985985]\n [ 0.05207201  0.13856235  0.25286996]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.7174439212190918, "std_reward": 0.3603065141056537, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-29T10:03:38.204711"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93cbbd91c12d4ca450cbcc2a8a2c2fd9a2e9acd73a62bbd40c16dfda604c5553
+size 2387